Detection of human activities in an image or video is of crucial importance and it is critical to determine the human presence for applications where automatic human body detection is a key enabler such as security and surveillance, robotics, surveillance and Intelligent Transport System, autonomous vehicle and automatic driver assistance system etc. Similarly, in computer vision systems, segmentation of image for detection of objects in each segment and differentiating human from other objects is still a challenge.
Large numbers of visual pattern that appear in an image increase the complexity. Human detection involves the ability of hardware and software to detect the presence of human in an image. Detection of human in an image currently is performed by using various human detection techniques and algorithms. Though such techniques and algorithms are widely used however results provided by said techniques or algorithms often contain large number of false predictions.
Many solutions have been proposed to address the problems associated with reduction of the false predictions or errors associated with the human detection and tracking techniques. One of the frequently followed techniques for detection of human is to combine plurality of human detection techniques in order to detect human in real time. However, the success of combination is affected by an error associated with each detection technique. One such solution has been disclosed in U.S. Pat. No. 7,162,076 of Chengjun Liu that teaches a vector to represent an image to be analyzed, from the DFA vector as processed using a Bayesian fusion Classifier technique. Although, the method discloses face detection with relatively low probability of error and false detection rate but it remains silent on determining the accuracy of the solution when more than one technique or algorithm is involved.
Therefore, there is a need in the art of a solution which is capable of reducing the false predictions of the plurality of techniques available for human detection by determining the accuracy of all the techniques applied for detection of human in an image.